This is exactly why we’re building MoltGhost. Local AI agents alone aren’t enough. You need private runtime, isolated compute, and wallet-native infrastructure to prevent access pattern leakage. Privacy for AI agents has to be full-stack.

Behind the scenes, our dev team is still building MoltGhost. Starting with the MoltGhost UI — some parts are coded manually, while others use AI to move faster. But not everything should be generated by AI. Honestly, we’re getting a bit bored with the generic AI-generated UI

Just shipped Llama 3.1 8B on MoltGhost 🦙 3 models now available: • Qwen 3 8B — all-rounder • Phi-4 Mini — fast & light • Llama 3.1 8B — strong reasoning One-click deploy. Dedicated GPU. No shared infra.

GM $MOLTG Still building MoltGhost. We’re currently working on several things behind the scenes. Our focus remains on improving the infrastructure and overall experience for running private AI agents on dedicated machines. More updates soon. 👻

x.com/i/article/2033…

Just dropped - moltghost-builder A lightweight VPS service that builds custom HuggingFace LLM Docker images on demand — pulls the model via Ollama, builds, pushes to Docker Hub, and fires a callback when done. github.com/Moltghost/molt… #nodejs #docker #llm #huggingface

We sincerely apologize for the lack of updates over the past few days. Due to unexpected natural disaster conditions in our working area, our operations were temporarily disrupted. Thankfully, the situation has now been resolved and everything is back on track. We are now fully

For now, we've disabled the free launch on the website. We're actively developing the new app manager at moltghost-app-manager.vercel.app your private way to deploy OpenClaw easily & securely.

MoltGhost Dexscreener just got an update. We’ve added GitHub and Docs so everyone can easily explore what we’re building and follow our progress. Check it out. dexscreener.com/solana/4gzndbr…

your AI agent shouldn’t leak your data - prompts - memory - workflows if it’s not private, it’s not yours

Inference is the most critical layer in OpenClaw. It’s not just “chat” — it’s the execution core. Every prompt, system message, file context, and tool output is sent into the model at this stage. If your inference endpoint points to external APIs, you’re not running a private

So instead of sending all that inference data outside your infra, we run it like this: OpenClaw → Qwen 3B → fully local In this demo, every prompt, system message, file context, and tool output stays inside the machine. No external inference endpoint No fallback to cloud No

I just published Self-Hosting Your AI Agent Gateway: Why ‘Running Locally’ Is Not Enough medium.com/p/self-hosting…

Most people focus on inference for private AI. But memory is where things actually stay. Every chat, file, and tool output becomes part of the agent’s long-term context. That’s why in MoltGhost next phase, we’re pushing: - per-agent memory isolation - local vector storage -

🔍 I just audited MoltGhost's own infrastructure against the privacy standard we laid out in our "Self-Hosting Your AI Agent Gateway" article. Honest score: ~60% private. Here's what's already running on our own server: ✅ Express gateway — every request routed locally ✅

Gm, deep in code audits, patching things up. Keep building, keep sipping ☕

Your AI agent runs as root. It can cat /tmp/startup.sh and see every secret you passed in. Filesystem security isn't optional — it's the difference between "isolated agent" and "open backdoor." Mount only what's needed. Read-only by default. Delete secrets after exec. We're

Filesystem Privacy & Security: The Forgotten Layer in AI Agent Deployment Why Filesystem Matters When we talk about AI security, the conversation usually gravitates toward prompt injection, model poisoning, or API key leaks. Rarely does anyone talk about the filesystem — the

My self-sovereign / local / private / secure LLM setup, April 2026 vitalik.eth.limo/general/2026/0…